Understanding the AI Hype Cycle in Healthcare

Overhyping healthcare trends often misses the bigger picture, dilutes the impact real innovation can have, and leaves a trail of broken promises and unfulfilled potential when hype fades. We've seen this play out in value-based care and digital health, and we may see the same thing happen in the current darlings: longevity/wellness and AI. All these concepts have merit. In fact, I'd say they're all critical to the future of healthcare. But, to mix metaphors, we're chasing sugar highs rather than building the foundation of a sustainable, nutritious foundation. 🔹 Longevity & Wellness When it comes to longevity, we're mis-framing the argument and chasing the wrong population. I've spent my career replacing hips and knees — one of the most effective "wellness" hacks in medicine. In my experience, patients don’t necessarily want to live longer; they want to live well later in life. In short, it's about quality, not quantity. The current longevity trend is being misrepresented as biohacking for the worried well. This is the wrong framing. The goals should be prevention, early detection, and focused, high-yield intervention, everything we expect from true primary care and a functioning health system. 🔹 Artificial Intelligence AI is booming, but there’s a general lack of direction. There are 60+ ambient AI scribe products. While investment dollars continue to flow in, no one is quite sure what the next use case of AI in healthcare will be. Improving back-office workflows with AI agents and automated campaigns are logical next steps. Deep analysis of patient data to surface insights and personalize care is another possibility. These areas are quickly becoming red oceans with an influx of investment dollars and startup entrants. AI in healthcare is becoming saturated and pricey. There are challenges: implementation complexity, cost, and safety chief among them. But there's another challenge that may not be getting enough attention — foundational models have gotten good, really good. Many front-end wrappers are starting to feel like expensive middlemen. One solution is to build AI tools in-house. Smaller, nimbler practices with motivated teams will move faster. Legacy systems anchored to massive EMRs, complex IT infrastructure, and multiple admin layers will struggle. Implementing AI from the ground up works better than retrofitting it on to existing processes. We're in the tastes great, less filling phase of longevity/wellness and AI. Hype is high, capital is flowing, and business models come later. We've been here before. Good feelings and empty calories don't build the future — they pass right through the system. #longevity #wellness #aiinhealthcare

As Judy Faulkner, CEO of Epic Systems, recently noted: "AI that doesn't improve care isn't worth implementing, regardless of the hype. The question isn't 'How do we use AI?' but rather 'How do we deliver better care, and might AI help us do that?'" In the rush to embrace artificial intelligence, many healthcare leaders are asking the wrong questions—and sometimes investing in the wrong solutions. That’s why I wrote this: 📄 "AI in Healthcare: Hype vs. Reality – What Works, What Doesn’t, and What’s Next" This isn’t another AI pitch or abstract vision piece. It’s a field-tested, executive-level guide to separating real value from vaporware. Inside, you'll find: ✅ The AI use cases that are truly delivering impact in clinical, financial, and operational settings ✅ Why 73% of AI pilots fail—and what successful systems are doing differently ✅ A roadmap for CIOs, CMIOs, and digital health leaders to move from pilot to enterprise-wide scale ✅ Emerging trends like multimodal AI, federated learning, and task-specific LLMs ✅ Actionable leadership strategies to evaluate, implement, and govern AI responsibly 📌 Whether you're a skeptic, enthusiast, or somewhere in between, this paper was written to help leaders ask better questions, align AI to real problems, and implement with clarity. Let’s stop chasing the magic. Let’s start building the future wisely. 🔗 Download the full white paper below 👇 #AIinHealthcare #DigitalHealth #HealthIT #Leadership #WisdomAtWork #HealthcareInnovation #CIO #CMIO #ResponsibleAI #JudyFaulkner #EpicSystems #KLAS #MayoClinic #HealthTech #AIethics

#AIAgents in #Healthcare (Part 4 of 5): Hype vs. Reality – Separating AI Promises from Clinical Proof Healthcare AI is frequently described as revolutionary, yet real-world effectiveness often lags behind enthusiastic marketing claims. Not all AI solutions have undergone rigorous clinical validation, and many lack peer-reviewed evidence of their effectiveness. Recent studies highlight significant challenges: Independent validation of AI tools remains rare. Fragmented healthcare data hampers real-world accuracy. Some AI systems underdeliver on promised efficiency gains. For example, recent research in AI-driven radiology found improved diagnostic accuracy by 15%, but also uncovered an 8% error rate from over-reliance on AI without sufficient clinician oversight. This underscores the importance of balancing AI tools with human expertise. Ensuring AI lives up to its promise requires: Robust clinical validation in practical settings. Peer-reviewed research confirming real-world efficacy. Continuous, long-term performance monitoring. Has your experience with healthcare AI met expectations, or has reality fallen short of the hype? Follow me to catch the final post in this series: "The Future of AI in Healthcare – Ethical and Practical Considerations." #HealthcareAI #AIinMedicine #ClinicalResearch #DigitalHealth #AIValidation #HealthTech #EvidenceBasedMedicine #DoctorGPT #DrGPT #ArtificialIntelligence